1. Technical Field
The present invention relates to semantic indexing, and, more particularly, to reducing ranking errors in semantic indexing systems and methods.
2. Description of the Related Art
Supervised Semantic Indexing (SSI) models are trained using a set of queries and documents regarded as good matches for the queries. There are several practical challenges that arise when applying this scheme. In particular, there are many sources of ranking errors that can affect the performance of the model. For example, two substantial problems that can cause ranking errors are a lack of training data and changes in the distribution of queries over time. Here, a lack of training data can cause the model to overfit the data. In addition, changes in query distributions may render the SSI model obsolete for new data.